Model Card for Model ID
Model Details
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
The model was trained using the ThinkSafe self-generated safety alignment methodology. See the paper for details on the training data generation process.
Training Procedure
This model uses LoRA (Low-Rank Adaptation) for efficient fine-tuning on top of the Qwen3-0.6B base model. The training follows the ThinkSafe framework for safety alignment in reasoning models.
Training Hyperparameters
- Training regime: Mixed precision training with PEFT/LoRA
Evaluation
Please refer to the
ThinkSafe paper for detailed evaluation results and methodology.
Testing Data, Factors & Metrics
Testing Data
See the paper for details on evaluation datasets and benchmarks used.
Metrics
The model was evaluated on safety benchmarks and reasoning tasks. Refer to the paper for specific metrics and results.
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
Citation
BibTeX:
1@article{lee2025thinksafe,
2 title={THINKSAFE: Self-Generated Safety Alignment for Reasoning Models},
3 author={Lee, Seanie and others},
4 journal={arXiv preprint arXiv:2601.23143},
5 year={2025}
6}
More Information
For more details, please refer to:
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
